We develop a technique for generalising from data in which models are samplers represented as program text. We establish encouraging empirical results that suggest that Markov chain Monte Carlo probabilistic programming inference techniques coupled with higher-order probabilistic programming languages are now sufficiently powerful to enable successful inference of this kind in nontrivial domain...

Abstract Gaussian processes are powerful non-parametric probabilistic models for stochastic functions. However, the direct implementation entails a complexity that is computationally intractable when number of observations large, especially estimated with fully Bayesian methods such as Markov chain Monte Carlo. In this paper, we focus on low-rank approximate processes, based basis function appr...

Abstract Complex Event Recognition (CER) systems detect event occurrences in streaming time-stamped input using predefined patterns. Logic-based approaches are of special interest CER, since, via Statistical Relational AI, they combine uncertainty-resilient reasoning with time and change, machine learning, thus alleviating the cost manual pattern authoring. We present a system based on Answer S...